High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

SED-MVS		78.97 1	84.56 1	72.45 1	81.70 2	86.20 2	77.82 4	59.97 5	88.89 1	65.96 1	86.00 5	84.02 1	70.03 1	76.19 4	76.17 5	79.22 19	94.46 1

DVP-MVS		77.54 2	84.41 2	69.54 6	79.93 3	86.08 3	77.20 9	60.31 3	88.62 2	62.54 2	86.67 3	83.77 2	58.04 34	75.84 7	75.69 8	79.21 20	94.17 2
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
SF-MVS		76.41 3	80.45 6	71.69 2	82.90 1	86.54 1	82.08 1	64.58 1	81.67 11	59.82 5	86.26 4	77.90 8	61.11 16	71.81 27	70.75 34	79.63 12	88.22 23

MSP-MVS		76.38 4	82.99 3	68.68 7	71.93 18	78.65 23	77.61 6	55.44 18	88.04 3	60.25 4	92.24 1	77.08 11	69.84 2	75.48 8	75.69 8	76.99 59	93.75 3
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
DVP-MVS++		75.99 5	81.32 5	69.77 5	71.86 20	85.13 4	77.62 5	59.87 7	82.69 10	61.55 3	83.05 9	79.63 6	69.78 3	76.01 5	75.89 6	77.92 41	86.86 35

DPE-MVS		75.74 6	82.82 4	67.49 11	77.07 7	82.01 8	77.05 10	57.70 11	86.55 5	55.44 16	90.50 2	82.52 3	60.33 20	72.99 15	72.98 16	77.33 50	92.19 6
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DPM-MVS		74.63 7	78.53 11	70.07 3	76.10 9	82.56 7	79.30 2	59.89 6	80.49 13	57.75 11	66.98 26	76.16 14	65.95 5	79.35 1	78.47 1	81.45 5	85.71 44

APDe-MVS		74.59 8	80.23 7	68.01 10	76.51 8	80.20 15	77.39 7	58.18 9	85.31 6	56.84 13	84.89 6	76.08 15	60.66 18	71.85 26	71.76 21	78.47 29	91.49 9
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MCST-MVS		74.06 9	77.71 14	69.79 4	78.95 4	81.99 9	76.33 11	62.16 2	75.89 19	52.96 24	64.37 31	73.30 22	65.66 6	77.49 2	77.43 3	82.67 1	93.51 4

CNVR-MVS		73.87 10	78.60 10	68.35 9	73.32 13	81.97 10	76.19 12	59.29 8	80.12 14	56.70 14	67.09 25	76.48 12	64.26 8	75.88 6	75.75 7	80.32 8	92.93 5

SMA-MVS		73.31 11	79.53 8	66.05 13	71.25 21	80.13 16	74.99 13	56.09 14	84.14 7	54.48 18	73.74 16	80.23 4	61.43 13	74.96 9	74.09 12	78.08 38	89.42 13
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CSCG		72.98 12	76.86 16	68.46 8	78.23 6	81.74 11	77.26 8	60.00 4	75.61 22	59.06 6	62.72 33	77.42 10	56.63 46	74.24 11	77.18 4	79.56 13	89.13 17

HPM-MVS++		72.44 13	78.73 9	65.11 14	71.88 19	77.31 34	71.98 21	55.67 16	83.11 9	53.59 22	75.90 12	78.49 7	61.00 17	73.99 12	73.31 15	76.55 63	88.97 18

APD-MVS		71.86 14	77.91 13	64.80 16	70.39 25	75.69 44	74.02 15	56.14 13	83.59 8	52.92 25	84.67 7	73.46 21	59.30 26	69.47 43	69.66 44	76.02 70	88.84 19
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_NAP		71.50 15	77.27 15	64.77 17	69.64 27	79.26 17	73.53 16	54.73 24	79.32 16	54.23 19	74.81 13	74.61 19	59.40 25	73.00 14	72.17 19	77.10 58	87.72 27

NCCC		71.36 16	75.44 18	66.60 12	72.46 16	79.18 19	74.16 14	57.83 10	76.93 17	54.19 20	63.47 32	71.08 26	61.30 15	73.56 13	73.70 13	79.69 11	90.19 10

train_agg		70.74 17	76.53 17	63.98 19	70.33 26	75.16 48	72.33 20	55.78 15	75.74 20	50.41 33	80.08 11	73.15 23	57.75 38	71.96 25	70.94 31	77.25 54	88.69 21

TSAR-MVS + MP.		70.28 18	75.09 19	64.66 18	69.34 29	64.61 133	72.60 19	56.29 12	80.73 12	58.36 9	84.56 8	75.22 17	55.37 53	69.11 49	69.45 45	75.97 72	81.97 79
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepPCF-MVS	62.48 1	70.07 19	78.36 12	60.39 41	62.38 59	76.96 37	65.54 58	52.23 32	87.46 4	49.07 34	74.05 15	76.19 13	59.01 28	72.79 19	71.61 23	74.13 112	89.49 12

SteuartSystems-ACMMP		69.78 20	74.76 20	63.98 19	73.45 12	78.56 24	73.13 18	55.24 21	70.68 34	48.93 36	70.43 21	69.10 28	54.00 59	72.78 21	72.98 16	79.14 21	88.74 20
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 21	72.84 22	63.98 19	68.87 33	75.09 49	71.87 22	51.22 36	73.50 26	58.17 10	68.05 24	68.67 29	57.79 37	70.49 36	69.23 47	75.98 71	84.84 56

SD-MVS		68.30 22	72.58 24	63.31 24	69.24 30	67.85 106	70.81 27	53.65 29	79.64 15	58.52 8	74.31 14	75.37 16	53.52 65	65.63 75	63.56 110	74.13 112	81.73 84
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
DELS-MVS		67.36 23	70.34 38	63.89 22	69.12 31	81.55 12	70.82 26	55.02 22	53.38 77	48.83 37	56.45 49	59.35 58	60.05 23	74.93 10	74.78 10	79.51 14	91.95 7
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MP-MVS		67.34 24	73.08 21	60.64 38	66.20 38	76.62 39	69.22 33	50.92 38	70.07 35	48.81 38	69.66 22	70.12 27	53.68 62	68.41 54	69.13 49	74.98 91	87.53 29
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DeepC-MVS	60.65 2	67.33 25	71.52 31	62.44 27	59.79 80	74.84 51	68.89 34	55.56 17	73.91 25	53.50 23	55.00 55	65.63 34	60.08 22	71.99 24	71.33 27	76.85 60	87.94 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 26	72.08 26	61.56 33	66.76 36	73.58 60	71.41 23	52.98 30	69.92 37	43.85 61	70.58 20	58.75 60	56.76 44	72.90 17	71.88 20	77.57 46	86.94 34

CANet		67.21 27	71.83 28	61.83 29	64.51 44	79.25 18	66.72 50	48.73 55	68.49 42	50.63 32	61.40 37	66.47 32	61.44 12	69.31 47	69.90 39	78.94 25	88.00 24

CDPH-MVS		67.03 28	71.64 29	61.65 32	69.10 32	76.84 38	71.35 25	55.42 19	67.02 45	42.83 66	65.27 30	64.60 38	53.16 68	69.70 42	71.40 25	78.02 40	86.67 36

MAR-MVS		66.85 29	69.81 39	63.39 23	73.56 11	80.51 14	69.87 29	51.51 35	67.78 44	46.44 47	51.09 69	61.60 52	60.38 19	72.67 22	73.61 14	78.59 26	81.44 88
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
DeepC-MVS_fast	60.18 3	66.84 30	70.69 36	62.36 28	62.76 53	73.21 63	67.96 37	52.31 31	72.26 29	51.03 27	56.50 48	64.26 39	63.37 9	71.64 28	70.85 32	76.70 62	86.10 41
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		66.77 31	72.21 25	60.44 40	61.23 68	70.00 86	64.26 62	47.79 68	72.98 27	56.32 15	71.35 19	72.33 24	55.68 52	65.49 76	66.66 71	77.35 48	86.62 37

MVS_0304		66.31 32	71.61 30	60.14 43	62.59 57	78.98 21	67.13 46	45.75 95	64.35 50	45.23 55	60.69 41	67.67 31	61.73 11	71.09 31	71.03 29	78.41 33	87.44 30

ACMMPR		66.20 33	71.51 32	60.00 45	65.34 42	74.04 55	69.39 31	50.92 38	71.97 30	46.04 49	66.79 27	65.68 33	53.07 69	68.93 51	69.12 50	75.21 85	84.05 62

3Dnovator	58.39 4	65.97 34	66.85 54	64.94 15	73.72 10	79.03 20	67.73 40	54.25 25	61.52 53	52.79 26	42.27 96	60.73 56	62.01 10	71.29 29	71.75 22	79.12 22	81.34 91

TSAR-MVS + ACMM		65.95 35	72.83 23	57.93 56	69.35 28	65.85 125	73.36 17	39.84 151	76.00 18	48.69 39	82.54 10	75.03 18	49.38 97	65.33 78	63.42 112	66.94 173	81.67 85

sasdasda		65.55 36	70.75 34	59.49 49	62.11 62	78.26 28	66.52 51	43.82 119	71.54 31	47.84 41	61.30 38	61.68 49	58.48 31	67.56 61	69.67 42	78.16 36	85.25 51

canonicalmvs		65.55 36	70.75 34	59.49 49	62.11 62	78.26 28	66.52 51	43.82 119	71.54 31	47.84 41	61.30 38	61.68 49	58.48 31	67.56 61	69.67 42	78.16 36	85.25 51

QAPM		65.47 38	67.82 46	62.72 26	72.56 14	81.17 13	67.43 43	55.38 20	56.07 70	43.29 64	43.60 91	65.38 36	59.10 27	72.20 23	70.76 33	78.56 27	85.59 48

PGM-MVS		65.35 39	70.43 37	59.43 51	65.78 40	73.75 57	69.41 30	48.18 64	68.80 41	45.37 53	65.88 29	64.04 40	52.68 76	68.94 50	68.68 55	75.18 86	82.93 69

PHI-MVS		65.17 40	72.07 27	57.11 66	63.02 51	77.35 33	67.04 47	48.14 66	68.03 43	37.56 92	66.00 28	65.39 35	53.19 67	70.68 33	70.57 36	73.72 120	86.46 40

CLD-MVS		64.69 41	67.25 48	61.69 31	68.22 35	78.33 26	63.09 66	47.59 71	69.64 38	53.98 21	54.87 56	53.94 76	57.87 35	72.79 19	71.34 26	79.40 16	69.87 161
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		64.66 42	67.11 51	61.80 30	71.04 22	77.91 30	62.75 69	54.78 23	51.43 80	47.54 43	53.77 59	54.85 73	56.84 42	70.59 34	71.50 24	77.86 42	89.70 11

EPNet		64.39 43	70.93 33	56.77 68	60.58 75	75.77 41	59.28 90	50.58 42	69.93 36	40.73 81	68.59 23	61.60 52	53.72 60	68.65 52	68.07 57	75.75 77	83.87 64
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 44	69.48 40	58.39 53	62.21 61	71.81 78	67.27 44	49.51 49	69.40 40	45.76 51	60.41 42	64.96 37	51.84 78	67.33 66	67.57 64	73.78 119	84.89 54

EC-MVSNet		64.30 45	68.19 42	59.76 47	62.97 52	75.31 47	67.26 45	44.19 113	60.73 56	47.52 44	55.84 51	62.12 47	57.67 39	70.71 32	67.47 65	78.97 24	85.13 53

casdiffmvs_mvg		64.26 46	67.60 47	60.36 42	62.26 60	78.54 25	69.39 31	48.33 62	56.54 65	45.36 54	52.86 63	57.36 65	58.42 33	70.28 37	70.24 38	78.43 30	87.39 32
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		63.87 47	67.08 52	60.12 44	60.90 71	78.29 27	67.91 38	48.01 67	55.89 72	44.97 56	50.45 71	56.94 66	59.54 24	70.17 40	69.81 40	79.41 15	87.99 25
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS_Test		63.75 48	67.24 49	59.68 48	60.01 76	76.99 36	68.13 36	45.17 99	57.45 64	43.74 62	53.07 62	56.16 71	61.33 14	70.27 38	71.11 28	79.72 10	85.63 47

X-MVS		63.53 49	68.62 41	57.60 60	64.77 43	73.06 64	65.82 56	50.53 43	65.77 47	42.02 74	58.20 46	63.42 43	47.83 108	68.25 58	68.50 56	74.61 101	83.16 68

ACMMP		63.27 50	67.85 45	57.93 56	62.64 56	72.30 75	68.23 35	48.77 54	66.50 46	43.05 65	62.07 34	57.84 63	49.98 89	66.58 70	66.46 77	74.93 92	83.17 66
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
CS-MVS		63.16 51	68.01 44	57.49 61	57.39 96	72.73 69	63.38 65	45.16 100	59.37 58	46.49 46	58.93 45	57.68 64	56.31 47	71.12 30	70.37 37	76.23 69	85.88 42

ETV-MVS		62.88 52	68.18 43	56.70 69	58.47 88	74.89 50	60.26 82	43.96 116	58.27 63	42.37 72	61.47 36	56.56 67	57.80 36	68.00 59	68.74 53	77.34 49	89.33 16

AdaColmap		62.79 53	62.63 70	62.98 25	70.82 23	72.90 67	67.84 39	54.09 27	65.14 48	50.71 30	41.78 98	47.64 104	60.17 21	67.41 65	66.83 69	74.28 107	76.69 115

3Dnovator+	55.76 7	62.70 54	65.10 62	59.90 46	65.89 39	72.15 76	62.94 68	49.82 48	62.77 52	49.06 35	43.62 90	61.47 54	58.60 30	68.51 53	66.75 70	73.08 134	80.40 99

OpenMVS	55.62 8	62.57 55	63.76 67	61.19 35	72.13 17	78.84 22	64.42 60	50.51 44	56.44 67	45.67 52	36.88 127	56.51 68	56.66 45	68.28 57	68.96 51	77.73 44	80.44 98

PVSNet_BlendedMVS		62.53 56	66.37 56	58.05 54	58.17 89	75.70 42	61.30 75	48.67 58	58.67 59	50.93 28	55.43 53	49.39 93	53.01 71	69.46 44	66.55 74	76.24 67	89.39 14

PVSNet_Blended		62.53 56	66.37 56	58.05 54	58.17 89	75.70 42	61.30 75	48.67 58	58.67 59	50.93 28	55.43 53	49.39 93	53.01 71	69.46 44	66.55 74	76.24 67	89.39 14

MVSTER		62.51 58	67.22 50	57.02 67	55.05 117	69.23 94	63.02 67	46.88 82	61.11 55	43.95 60	59.20 44	58.86 59	56.80 43	69.13 48	70.98 30	76.41 65	82.04 76

CHOSEN 1792x2688		62.48 59	64.06 66	60.64 38	72.50 15	84.18 5	62.43 70	53.77 28	47.90 94	39.85 85	25.15 190	44.76 118	53.72 60	77.29 3	77.61 2	81.60 4	91.53 8

CostFormer		62.45 60	65.68 60	58.67 52	63.29 48	77.65 31	67.62 41	38.42 162	54.04 75	46.00 50	48.27 79	57.89 62	56.97 41	67.03 68	67.79 63	79.74 9	87.09 33

PCF-MVS	55.99 6	62.31 61	66.60 55	57.32 64	59.12 87	73.68 59	67.53 42	48.71 56	61.35 54	42.83 66	51.33 68	63.48 42	53.48 66	65.64 74	64.87 94	72.22 139	85.83 43
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
diffmvs		62.30 62	66.05 58	57.92 58	57.08 98	75.60 46	66.90 48	47.06 80	55.45 74	43.37 63	53.45 61	55.60 72	57.21 40	66.57 71	68.00 59	75.89 75	87.70 28
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DI_MVS_plusplus_trai		61.86 63	65.26 61	57.90 59	57.93 93	74.51 53	66.30 53	46.49 88	49.96 84	41.62 77	42.69 94	61.77 48	58.74 29	70.25 39	69.32 46	76.31 66	88.30 22

MSLP-MVS++		61.81 64	62.19 75	61.37 34	68.33 34	63.08 147	70.75 28	38.89 158	63.96 51	57.51 12	48.59 77	61.66 51	53.67 63	62.04 121	59.92 156	79.03 23	76.08 118

CS-MVS-test		61.68 65	65.97 59	56.67 70	57.77 94	72.59 72	57.63 97	45.54 97	58.53 62	47.11 45	59.45 43	56.34 69	55.15 54	64.52 88	65.03 92	76.80 61	85.34 50

OPM-MVS		61.59 66	62.30 74	60.76 37	66.53 37	73.35 62	71.41 23	54.18 26	40.82 125	41.57 78	45.70 85	54.84 74	54.43 58	69.92 41	69.19 48	76.45 64	82.25 73
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MS-PatchMatch		61.41 67	61.88 78	60.85 36	70.57 24	75.98 40	66.29 54	46.91 81	50.56 82	48.28 40	36.30 130	51.64 80	50.95 84	72.89 18	70.65 35	82.13 3	75.17 126

EIA-MVS		60.56 68	64.29 65	56.20 75	59.14 86	72.68 71	59.55 88	43.56 123	51.78 79	41.01 80	55.47 52	51.93 79	55.87 49	65.01 82	66.57 73	78.06 39	86.60 39

ACMP	56.21 5	59.78 69	61.81 80	57.41 63	61.15 69	68.88 96	65.98 55	48.85 53	58.56 61	44.19 59	48.89 75	46.31 110	48.56 102	63.61 104	64.49 102	75.75 77	81.91 80
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		59.69 70	62.59 71	56.31 73	61.94 64	68.15 103	66.90 48	48.15 65	59.75 57	38.47 88	50.38 72	48.34 101	46.87 113	65.39 77	64.93 93	75.51 81	81.21 93

Effi-MVS+		59.63 71	61.78 81	57.12 65	61.56 65	71.63 79	63.61 63	47.59 71	47.18 95	37.79 89	45.29 86	49.93 89	56.27 48	67.45 63	67.06 67	75.91 73	83.93 63

CPTT-MVS		59.54 72	64.47 64	53.79 85	54.99 119	67.63 109	65.48 59	44.59 107	64.81 49	37.74 90	51.55 66	59.90 57	49.77 93	61.83 123	61.26 140	70.18 154	84.31 61

baseline2		59.20 73	61.72 82	56.27 74	59.61 82	74.12 54	58.65 93	49.42 50	48.10 92	40.12 84	49.10 74	44.15 120	51.24 81	66.65 69	67.88 62	78.56 27	82.06 75

MGCFI-Net		59.19 74	66.89 53	50.20 113	57.15 97	68.62 99	54.79 122	39.20 156	70.99 33	32.93 115	60.83 40	61.00 55	45.54 119	63.77 102	60.71 148	71.59 143	82.29 71

GeoE		58.97 75	60.94 83	56.67 70	61.27 67	72.71 70	61.35 74	45.69 96	49.19 88	41.22 79	39.55 114	49.58 92	52.79 75	64.79 84	65.89 81	77.73 44	84.87 55

baseline		58.65 76	61.99 76	54.75 80	54.70 121	71.85 77	60.20 83	43.91 117	55.99 71	40.13 83	53.50 60	50.91 86	55.76 50	61.29 131	61.73 132	73.83 116	78.68 107

PVSNet_Blended_VisFu		58.56 77	62.33 73	54.16 82	56.90 99	73.92 56	57.72 96	46.16 93	44.23 103	42.73 69	46.26 82	51.06 85	46.28 116	67.99 60	65.38 87	75.18 86	87.44 30

ACMM	53.73 9	57.91 78	58.27 98	57.49 61	63.10 49	66.45 119	65.65 57	49.02 52	53.69 76	42.67 70	36.41 129	46.07 113	50.38 87	64.74 86	64.63 99	74.14 111	75.91 119
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 79	63.63 68	51.15 101	52.18 128	70.20 85	58.14 95	37.32 169	56.49 66	31.06 125	57.38 47	50.05 88	53.67 63	64.98 83	65.04 91	74.57 102	81.29 92

ET-MVSNet_ETH3D		57.84 80	61.91 77	53.09 88	32.91 210	74.53 52	63.51 64	46.80 84	46.52 97	36.14 98	56.00 50	46.20 111	64.41 7	60.75 139	66.99 68	74.79 93	82.35 70

tpm cat1		57.41 81	58.26 99	56.42 72	60.80 73	72.56 73	64.35 61	38.43 161	49.18 89	46.36 48	36.69 128	43.50 124	54.47 56	61.39 129	62.64 120	74.11 114	81.81 81

IB-MVS	53.15 10	57.33 82	59.02 90	55.37 77	60.83 72	77.11 35	54.51 123	50.10 47	43.22 109	42.82 68	40.50 104	37.61 145	44.67 129	59.27 153	69.81 40	79.29 18	85.59 48
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
tpmrst		57.23 83	59.08 89	55.06 78	59.91 78	70.65 83	60.71 78	35.38 180	47.91 93	42.58 71	39.78 109	45.45 115	54.44 57	62.19 118	62.82 117	77.37 47	84.73 57

baseline1		57.21 84	60.53 85	53.33 87	62.50 58	69.86 88	57.33 101	50.59 41	43.39 108	30.00 131	48.60 76	51.09 84	42.36 142	69.38 46	68.03 58	77.20 55	73.39 134

FA-MVS(training)		57.15 85	60.42 86	53.34 86	58.15 91	72.77 68	59.79 86	38.68 159	49.01 90	36.56 97	40.79 102	45.44 116	53.04 70	65.23 81	67.93 61	73.82 117	81.80 83

HyFIR lowres test		57.12 86	59.11 88	54.80 79	61.55 66	77.55 32	59.02 91	45.00 102	41.84 122	33.93 110	22.44 197	49.16 96	51.02 83	68.39 55	68.71 54	78.26 35	85.70 46

MVS_111021_LR		57.06 87	60.60 84	52.93 89	56.25 104	65.14 131	55.16 120	41.21 142	52.32 78	44.89 57	53.92 58	49.27 95	52.16 77	61.46 127	60.54 149	67.92 166	81.53 87

DCV-MVSNet		56.80 88	58.96 91	54.28 81	59.96 77	66.74 117	60.37 81	44.87 104	41.01 124	36.81 95	47.57 80	47.87 103	48.23 105	64.41 90	65.17 89	75.45 82	79.95 101

Anonymous20231211		56.40 89	57.00 110	55.70 76	59.78 81	72.49 74	61.29 77	46.83 83	40.50 127	40.46 82	22.12 199	49.73 90	51.07 82	64.39 91	65.30 88	74.74 95	84.44 60

PMMVS		55.74 90	62.68 69	47.64 133	44.34 178	65.58 129	47.22 161	37.96 165	56.43 68	34.11 108	61.51 35	47.41 105	54.55 55	65.88 73	62.49 124	67.67 168	79.48 102

Fast-Effi-MVS+		55.73 91	58.26 99	52.76 90	54.33 122	68.19 102	57.05 102	34.66 182	46.92 96	38.96 87	40.53 103	41.55 133	55.69 51	65.31 79	65.99 78	75.90 74	79.34 103

FC-MVSNet-train		55.68 92	57.00 110	54.13 83	63.37 46	66.16 121	46.77 164	52.14 33	42.36 116	37.67 91	48.50 78	41.42 135	51.28 80	61.58 126	63.22 114	73.56 122	75.76 122

FMVSNet3		55.66 93	59.68 87	50.96 103	50.59 142	66.49 118	57.57 98	46.61 85	49.30 85	28.77 136	39.61 110	51.42 81	43.85 134	68.29 56	68.80 52	78.35 34	73.86 129

OMC-MVS		55.48 94	61.85 79	48.04 132	41.55 185	60.32 165	56.80 106	31.78 202	75.67 21	42.30 73	51.52 67	54.15 75	49.91 91	60.28 144	57.59 163	65.91 176	73.42 132

tpm		54.94 95	57.86 104	51.54 99	59.48 84	67.04 113	58.34 94	34.60 184	41.93 121	34.41 105	42.40 95	47.14 106	49.07 100	61.46 127	61.67 136	73.31 129	83.39 65

GBi-Net		54.66 96	58.42 96	50.26 111	49.36 151	65.81 126	56.80 106	46.61 85	49.30 85	28.77 136	39.61 110	51.42 81	42.71 138	64.25 94	65.54 83	77.32 51	73.03 137

test1		54.66 96	58.42 96	50.26 111	49.36 151	65.81 126	56.80 106	46.61 85	49.30 85	28.77 136	39.61 110	51.42 81	42.71 138	64.25 94	65.54 83	77.32 51	73.03 137

test-LLR		54.62 98	58.66 94	49.89 118	51.68 134	65.89 123	47.88 155	46.35 89	42.51 113	29.84 132	41.41 99	48.87 97	45.20 122	62.91 112	64.43 103	78.43 30	84.62 58

dmvs_re		54.51 99	57.04 109	51.56 98	56.51 102	62.63 151	55.56 116	50.45 45	45.31 99	24.75 153	43.94 89	39.99 140	42.74 137	66.53 72	65.44 86	79.33 17	75.46 124

TSAR-MVS + COLMAP		54.37 100	62.43 72	44.98 148	34.33 206	58.94 172	54.11 128	34.15 193	74.06 24	34.57 104	71.63 18	42.03 132	47.88 107	61.26 132	57.33 166	64.83 179	71.74 147

EPMVS		54.07 101	56.06 116	51.75 97	56.74 101	70.80 81	55.32 118	34.20 190	46.46 98	36.59 96	40.38 106	42.55 127	49.77 93	61.25 133	60.90 144	77.86 42	70.08 158

v2v482		54.00 102	55.12 123	52.69 92	51.73 133	69.42 93	60.65 79	45.09 101	34.56 158	33.73 113	35.29 133	35.36 155	49.92 90	64.05 100	65.16 90	75.00 90	81.98 78

CNLPA		54.00 102	57.08 108	50.40 110	49.83 148	61.75 156	53.47 131	37.27 170	74.55 23	44.85 58	33.58 145	45.42 117	52.94 74	58.89 155	53.66 185	64.06 182	71.68 148

FMVSNet2		53.94 104	57.29 106	50.03 115	49.36 151	65.81 126	56.80 106	45.95 94	43.13 110	28.04 140	35.68 131	48.18 102	42.71 138	67.23 67	67.95 60	77.32 51	73.03 137

v8		53.77 105	54.82 128	52.54 93	52.12 129	66.95 116	60.56 80	43.23 129	37.17 147	35.35 100	34.96 136	37.50 147	49.51 96	63.67 103	64.59 100	74.48 104	78.91 106

GA-MVS		53.77 105	56.41 115	50.70 105	51.63 136	69.96 87	57.55 99	44.39 108	34.31 159	27.15 142	40.99 101	36.40 151	47.65 110	67.45 63	67.16 66	75.83 76	78.60 108

Effi-MVS+-dtu		53.63 107	54.85 127	52.20 95	59.32 85	61.33 159	56.42 112	40.24 149	43.84 105	34.22 107	39.49 115	46.18 112	53.00 73	58.72 159	57.49 165	69.99 157	76.91 113

thisisatest0530		53.61 108	57.22 107	49.40 123	51.30 138	68.22 101	52.72 139	43.34 127	42.72 112	35.31 101	43.57 92	44.14 121	44.37 132	63.00 110	64.86 95	69.34 160	74.00 128

v1144		53.47 109	54.65 129	52.10 96	51.93 131	69.81 89	59.32 89	44.77 106	33.21 165	32.52 117	33.55 146	34.34 163	49.29 98	64.58 87	64.81 97	74.74 95	82.27 72

v10		53.44 110	54.40 130	52.31 94	52.08 130	66.99 114	59.68 87	43.41 124	35.90 153	34.30 106	33.98 143	35.56 153	50.10 88	64.39 91	64.67 98	74.32 105	79.30 104

PatchmatchNet		53.37 111	55.62 121	50.75 104	55.93 111	70.54 84	51.39 144	36.41 173	44.85 101	37.26 93	39.40 117	42.54 128	47.83 108	60.29 143	60.88 146	75.69 79	70.87 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test2506		53.36 112	57.36 105	48.68 128	55.53 113	68.11 104	54.31 125	46.25 91	43.54 106	22.21 165	40.19 107	43.69 123	36.56 155	64.15 98	65.94 79	77.20 55	75.91 119

IterMVS-LS		53.36 112	55.65 120	50.68 107	55.34 115	59.04 170	55.00 121	39.98 150	38.72 135	33.22 114	44.52 88	47.05 107	49.63 95	61.82 124	61.77 131	70.92 149	76.61 117
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 114	58.66 94	47.04 136	44.94 172	65.89 123	47.88 155	35.95 176	42.51 113	29.84 132	41.41 99	48.87 97	45.20 122	62.91 112	64.43 103	78.43 30	84.62 58

tttt0517		53.05 115	56.73 114	48.76 126	50.35 144	67.51 110	51.96 143	43.34 127	42.00 120	33.88 111	43.19 93	43.49 125	44.37 132	62.58 117	64.86 95	68.67 162	73.46 131

MDTV_nov1_ep13		52.99 116	55.59 122	49.95 117	54.08 123	70.69 82	56.47 111	38.42 162	42.78 111	30.19 130	39.56 113	43.31 126	45.78 118	60.07 148	62.11 128	74.74 95	70.62 153

EPP-MVSNet		52.91 117	58.91 92	45.91 141	54.99 119	68.84 97	49.27 150	42.71 136	37.53 141	20.20 173	46.09 83	56.19 70	36.90 153	61.37 130	60.90 144	71.41 144	81.41 89

dps		52.84 118	52.92 141	52.74 91	59.89 79	69.49 92	54.47 124	37.38 168	42.49 115	39.53 86	35.33 132	32.71 168	51.83 79	60.45 140	61.12 141	73.33 128	68.86 167

v1192		52.69 119	53.86 133	51.31 100	51.22 139	69.76 90	57.37 100	44.39 108	32.21 168	31.39 124	32.41 154	32.44 171	49.19 99	64.25 94	64.17 105	74.31 106	81.81 81

V42		52.63 120	55.08 124	49.76 120	44.93 173	67.49 112	60.19 84	42.13 139	37.21 146	34.08 109	34.57 139	37.30 148	47.29 111	63.48 106	64.15 106	69.96 158	81.38 90

MSDG		52.58 121	51.40 154	53.95 84	65.48 41	64.31 141	61.44 73	44.02 114	44.17 104	32.92 116	30.40 167	31.81 175	46.35 115	62.13 119	62.55 122	73.49 124	64.41 175

ECVR-MVS		52.52 122	55.88 118	48.60 129	55.53 113	68.11 104	54.31 125	46.25 91	43.54 106	21.75 167	32.76 151	39.83 143	36.56 155	64.15 98	65.94 79	77.20 55	76.81 114

Fast-Effi-MVS+-dtu		52.47 123	55.89 117	48.48 130	56.25 104	65.07 132	58.75 92	23.79 213	41.27 123	27.07 144	37.95 122	41.34 136	50.85 85	62.90 114	62.34 126	74.17 110	80.37 100

v144192		52.43 124	53.63 135	51.03 102	51.06 140	69.60 91	56.94 104	44.84 105	32.15 169	30.88 126	32.45 153	32.71 168	48.36 103	62.98 111	63.52 111	74.10 115	82.02 77

thres100view900		52.33 125	53.91 132	50.48 109	56.10 106	67.79 107	56.18 114	49.18 51	35.86 155	25.22 150	34.74 137	34.10 164	42.41 141	64.45 89	62.62 121	73.81 118	77.85 109

v1921920		51.95 126	53.19 137	50.51 108	50.82 141	69.14 95	55.45 117	44.34 112	31.53 173	30.53 128	31.96 156	31.67 176	48.31 104	63.12 108	63.28 113	73.59 121	81.60 86

v148		51.72 127	53.15 138	50.05 114	50.15 146	67.51 110	56.98 103	42.85 134	32.60 167	32.41 119	33.88 144	34.71 160	44.45 130	61.06 134	63.00 116	73.45 125	79.24 105

TAPA-MVS	47.92 11	51.66 128	57.88 103	44.40 151	36.46 200	58.42 175	53.82 130	30.83 203	69.51 39	34.97 103	46.90 81	49.67 91	46.99 112	58.00 162	54.64 181	63.33 188	68.00 169
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
IS_MVSNet		51.53 129	57.98 102	44.01 155	55.96 110	66.16 121	47.65 157	42.84 135	39.82 130	19.09 181	44.97 87	50.28 87	27.20 188	63.43 107	63.84 107	71.33 146	77.33 111

v1240		51.42 130	52.66 143	49.97 116	50.31 145	68.70 98	54.05 129	43.85 118	30.78 177	30.22 129	31.43 160	31.03 183	47.98 106	62.62 116	63.16 115	73.40 126	80.93 95

pmmvs4		51.28 131	52.50 145	49.85 119	49.54 150	63.02 148	52.83 138	43.41 124	44.65 102	35.71 99	34.38 140	32.25 172	45.14 125	60.21 147	60.03 153	72.44 138	72.98 140

Vis-MVSNet		51.13 132	58.04 101	43.06 161	47.68 158	67.71 108	49.10 151	39.09 157	37.75 139	22.57 162	51.03 70	48.78 99	32.42 173	62.12 120	61.80 130	67.49 170	77.12 112
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 133	58.79 93	42.00 167	40.59 187	65.32 130	46.65 166	39.26 154	39.90 129	27.30 141	54.12 57	52.03 78	30.93 177	59.85 150	59.62 158	67.23 172	80.70 96
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
tfpn200view9		50.91 134	52.45 146	49.11 125	56.10 106	64.53 136	53.06 135	47.31 76	35.86 155	25.22 150	34.74 137	34.10 164	41.08 144	60.84 136	61.37 138	71.90 142	75.70 123

SCA		50.88 135	53.70 134	47.59 134	55.99 108	55.81 184	43.14 178	33.45 196	45.16 100	37.14 94	41.83 97	43.82 122	44.43 131	60.37 141	60.02 154	71.38 145	68.90 166

gg-mvs-nofinetune		50.82 136	55.83 119	44.97 149	60.63 74	75.69 44	53.40 132	34.48 186	20.05 212	6.93 208	18.27 206	52.70 77	33.57 163	70.50 35	72.93 18	80.84 6	80.68 97

thres200		50.76 137	52.52 144	48.70 127	55.98 109	64.60 134	55.29 119	47.34 74	33.91 162	24.36 154	34.33 141	33.90 166	37.27 151	60.84 136	62.41 125	71.99 140	77.63 110

test1111		50.62 138	54.98 126	45.55 144	53.84 125	68.48 100	48.99 152	47.25 77	40.60 126	15.64 189	31.51 159	38.32 144	33.01 170	64.34 93	66.62 72	74.55 103	74.95 127

thres400		50.39 139	52.22 147	48.26 131	55.02 118	66.32 120	52.97 136	48.33 62	32.68 166	22.94 160	33.21 148	33.38 167	37.27 151	62.74 115	61.38 137	73.04 135	75.81 121

EG-PatchMatch MVS		50.23 140	50.89 157	49.47 121	59.54 83	70.88 80	52.46 140	44.01 115	26.22 198	31.91 120	24.97 191	31.45 179	33.48 165	64.79 84	66.51 76	75.40 83	71.39 150

IterMVS		50.23 140	53.27 136	46.68 137	47.59 160	60.58 163	53.10 134	36.62 172	36.07 151	25.89 147	39.42 116	40.05 139	43.65 135	60.22 146	61.35 139	73.23 130	75.23 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 142	52.78 142	47.06 135	45.56 169	63.56 144	54.22 127	43.74 121	34.10 161	25.37 149	29.79 173	42.06 131	38.70 147	64.25 94	65.54 83	74.75 94	70.18 157

ACMH	47.82 13	50.10 143	49.60 163	50.69 106	63.36 47	66.99 114	56.83 105	52.13 34	31.06 176	17.74 186	28.22 179	26.24 199	45.17 124	60.88 135	63.80 108	68.91 161	70.00 160
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 144	56.99 112	41.52 170	52.79 126	57.06 178	41.44 183	43.13 130	56.13 69	19.24 180	52.11 64	48.38 100	22.14 195	58.19 161	58.38 161	70.35 152	68.71 168
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 145	49.75 162	49.40 123	55.88 112	59.86 167	56.31 113	45.33 98	48.57 91	28.32 139	31.54 158	36.81 150	46.27 117	57.17 167	55.88 176	64.29 181	58.42 193

UniMVSNet_NR-MVSNet		49.56 146	53.04 139	45.49 145	51.59 137	64.42 140	46.97 162	51.01 37	37.87 137	16.42 187	39.87 108	34.91 159	33.43 167	59.59 151	62.70 118	73.52 123	71.94 143

CDS-MVSNet		49.25 147	53.97 131	43.75 157	47.53 161	64.53 136	48.59 153	42.27 138	33.77 163	26.64 145	40.46 105	42.26 130	30.01 180	61.77 125	61.71 133	67.48 171	73.28 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 148	51.75 150	46.10 140	42.78 183	55.60 187	53.11 133	34.46 187	55.69 73	32.47 118	34.16 142	41.45 134	48.91 101	57.13 168	54.09 182	64.84 178	64.10 176
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 149	48.86 169	49.44 122	56.75 100	62.01 155	56.62 110	47.55 73	37.49 142	23.98 155	26.68 184	29.46 190	43.12 136	57.45 166	58.85 160	68.62 163	70.05 159

NR-MVSNet		48.84 150	51.76 149	45.44 146	57.66 95	60.64 161	47.39 158	47.63 69	37.26 143	13.31 192	37.31 124	29.64 189	33.53 164	63.52 105	62.09 129	73.10 133	71.89 146

CR-MVSNet		48.82 151	51.85 148	45.29 147	46.74 163	55.95 182	52.06 141	34.21 188	42.17 117	31.74 121	32.92 150	42.53 129	45.00 126	58.80 156	61.11 142	61.99 193	69.47 162

thres600view7		48.44 152	50.23 160	46.35 139	54.05 124	64.60 134	50.18 147	47.34 74	31.73 172	20.74 171	32.28 155	32.62 170	33.79 162	60.84 136	56.11 174	71.99 140	73.40 133

test-mter		48.31 153	55.04 125	40.45 174	34.12 207	59.02 171	41.77 182	28.05 207	38.43 136	22.67 161	39.35 118	44.40 119	41.88 143	60.30 142	61.68 135	74.20 108	82.12 74

PatchT		48.11 154	51.27 156	44.43 150	50.13 147	61.58 157	33.59 196	32.92 198	40.38 128	31.74 121	30.60 166	36.93 149	45.00 126	58.80 156	61.11 142	73.19 131	69.47 162

TranMVSNet+NR-MVSNet		48.06 155	51.36 155	44.21 153	50.38 143	62.09 154	47.28 159	50.88 40	36.11 150	13.25 193	37.51 123	31.60 178	30.70 178	59.34 152	62.53 123	72.81 136	70.31 155

TransMVSNet (Re)		47.46 156	48.94 168	45.74 143	57.96 92	64.29 142	48.26 154	48.47 61	26.33 197	19.33 178	29.45 176	31.28 182	25.31 192	63.05 109	62.70 118	75.10 89	65.47 173

DU-MVS		47.33 157	50.86 158	43.20 160	44.43 176	60.64 161	46.97 162	47.63 69	37.26 143	16.42 187	37.31 124	31.39 180	33.43 167	57.53 164	59.98 155	70.35 152	71.94 143

v7n		47.22 158	48.38 170	45.87 142	48.20 157	63.58 143	50.69 145	40.93 146	26.60 196	26.44 146	26.52 185	29.65 188	38.19 149	58.22 160	60.23 152	70.79 150	73.83 130

UA-Net		47.19 159	53.02 140	40.38 175	55.31 116	60.02 166	38.41 189	38.68 159	36.42 149	22.47 164	51.95 65	58.72 61	25.62 191	54.11 180	53.40 186	61.79 194	56.51 196

Baseline_NR-MVSNet		47.14 160	50.83 159	42.84 163	44.43 176	63.31 146	44.50 174	50.36 46	37.71 140	11.25 198	30.84 163	32.09 173	30.96 176	57.53 164	63.73 109	75.53 80	70.60 154

pmmvs5		47.02 161	50.02 161	43.51 159	43.48 181	62.65 150	47.24 160	37.78 167	30.59 178	24.80 152	35.26 134	30.43 184	34.36 160	59.05 154	60.28 151	73.40 126	71.92 145

UniMVSNet (Re)		46.89 162	51.65 152	41.34 172	45.60 168	62.71 149	44.05 175	47.10 79	37.24 145	13.55 191	36.90 126	34.54 162	26.76 189	57.56 163	59.90 157	70.98 148	72.69 141

thisisatest0515		46.88 163	49.57 164	43.74 158	45.33 171	60.46 164	46.19 168	41.06 145	30.34 179	29.73 134	32.50 152	31.63 177	35.43 158	58.75 158	61.71 133	64.70 180	71.59 149

tfpnnormal		46.61 164	46.82 177	46.37 138	52.70 127	62.31 152	50.39 146	47.17 78	25.74 200	21.80 166	23.13 195	24.15 207	33.45 166	60.28 144	60.77 147	72.70 137	71.39 150

pm-mvs1		46.14 165	49.34 167	42.41 164	48.93 154	62.22 153	44.98 172	42.68 137	27.66 190	20.76 170	29.88 172	34.96 158	26.41 190	60.03 149	60.42 150	70.70 151	70.20 156

IterMVS-SCA-FT		45.87 166	51.55 153	39.24 178	46.22 164	59.43 168	52.89 137	31.93 199	36.01 152	23.68 156	38.86 119	39.88 142	39.05 146	56.25 173	58.17 162	41.70 214	72.25 142

MIMVSNet		45.62 167	49.56 165	41.02 173	38.17 191	64.43 139	49.48 149	35.43 179	36.53 148	20.06 175	22.58 196	35.16 157	28.75 185	61.97 122	62.20 127	74.20 108	64.07 177

gm-plane-assit		45.41 168	48.03 172	42.34 165	56.49 103	40.48 212	24.54 216	34.15 193	14.44 219	6.59 209	17.82 207	35.32 156	49.82 92	72.93 16	74.11 11	82.47 2	81.12 94

ADS-MVSNet		45.39 169	46.42 178	44.19 154	48.74 156	57.52 176	43.91 176	31.93 199	35.89 154	27.11 143	30.12 168	32.06 174	45.30 120	53.13 186	55.19 178	68.15 165	61.07 185

GG-mvs-BLEND		44.87 170	64.59 63	21.86 211	0.01 229	73.70 58	55.99 115	0.01 225	50.70 81	0.01 230	49.18 73	63.61 41	0.01 225	63.83 101	64.50 101	75.13 88	86.62 37

pmmvs-eth3d		44.67 171	45.27 183	43.98 156	42.56 184	55.72 186	44.97 173	40.81 147	31.96 171	29.13 135	26.09 187	25.27 204	36.69 154	55.13 177	56.62 171	69.68 159	66.12 172

MDTV_nov1_ep13_2view		44.44 172	45.75 181	42.91 162	46.13 165	63.43 145	46.53 167	34.20 190	29.08 185	19.95 176	26.23 186	27.89 194	35.88 157	53.36 185	56.43 172	74.74 95	63.86 178

CMPMVS	33.64 16	44.39 173	46.41 179	42.03 166	44.21 179	56.50 180	46.73 165	26.48 212	34.20 160	35.14 102	24.22 192	34.64 161	40.52 145	56.50 172	56.07 175	59.12 198	62.74 181
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 174	51.67 151	35.72 188	51.82 132	55.24 188	34.57 195	41.63 140	39.10 133	8.84 205	45.93 84	46.63 109	14.45 205	54.09 181	57.03 168	63.00 189	63.65 179

TAMVS		44.27 175	49.35 166	38.35 182	44.74 174	61.04 160	39.07 187	31.82 201	29.95 181	18.34 184	33.55 146	39.94 141	30.01 180	56.85 170	57.58 164	66.13 175	66.54 170

MVS-HIRNet		43.98 176	43.63 187	44.39 152	47.66 159	59.31 169	32.66 202	33.88 195	30.15 180	33.75 112	16.82 212	28.39 193	45.25 121	53.92 184	55.00 180	73.16 132	61.80 182

UniMVSNet_ETH3D		43.97 177	46.01 180	41.59 168	38.31 190	56.20 181	49.69 148	38.18 164	28.18 186	19.88 177	27.82 181	30.20 185	33.41 169	54.18 179	56.30 173	70.05 156	69.17 164

RPMNet		43.70 178	48.17 171	38.48 181	45.52 170	55.95 182	37.66 190	26.63 211	42.17 117	25.47 148	29.59 175	37.61 145	33.87 161	50.85 191	52.02 190	61.75 195	69.00 165

PatchMatch-RL		43.37 179	44.93 184	41.56 169	37.94 192	51.70 190	40.02 185	35.75 177	39.04 134	30.71 127	35.14 135	27.43 196	46.58 114	51.99 187	50.55 194	58.38 200	58.64 191

FMVSNet5		43.29 180	47.07 175	38.87 179	30.46 212	50.99 192	45.87 169	37.19 171	42.17 117	19.32 179	26.77 183	40.51 137	30.26 179	56.82 171	55.81 177	70.10 155	56.46 197

test0.0.03 1		43.07 181	46.95 176	38.54 180	51.68 134	58.77 173	35.28 191	46.35 89	32.05 170	12.44 194	28.53 178	35.52 154	14.40 206	57.12 169	56.93 169	71.11 147	59.69 187

anonymousdsp		43.03 182	47.19 174	38.18 183	36.00 202	56.92 179	38.44 188	34.56 185	24.22 202	22.53 163	29.69 174	29.92 186	35.21 159	53.96 183	58.98 159	62.32 192	76.66 116

USDC		42.80 183	45.57 182	39.58 176	34.55 205	51.13 191	42.61 179	36.21 174	39.59 131	23.65 157	33.13 149	20.87 213	37.86 150	55.35 176	57.16 167	62.61 190	61.75 183

pmnet_mix02		42.41 184	43.24 189	41.44 171	45.80 167	57.46 177	42.19 180	41.57 141	29.38 183	23.39 158	26.08 188	23.96 208	27.31 187	51.50 188	53.76 184	68.36 164	60.58 186

CHOSEN 280x420		42.39 185	47.40 173	36.54 186	33.56 208	39.66 215	40.67 184	26.88 210	34.66 157	18.03 185	30.09 169	45.59 114	44.82 128	54.46 178	54.00 183	55.28 207	73.32 135

pmmvs6		41.90 186	44.01 186	39.43 177	44.45 175	58.77 173	41.92 181	39.22 155	21.74 205	19.08 182	17.40 210	31.33 181	24.28 194	55.94 174	56.67 170	67.60 169	66.24 171

Anonymous20231206		40.63 187	43.29 188	37.53 184	48.88 155	55.81 184	34.99 192	44.98 103	28.16 187	10.16 202	17.26 211	27.50 195	18.28 199	54.00 182	55.07 179	67.85 167	65.23 174

CVMVSNet		38.91 188	44.49 185	32.40 197	34.57 204	47.20 203	34.81 193	34.20 190	31.45 174	8.95 204	38.86 119	36.38 152	24.30 193	47.77 195	46.94 206	57.59 202	62.85 180

COLMAP_ROB	34.79 15	38.65 189	40.72 192	36.23 187	36.41 201	49.22 199	45.51 171	27.60 209	37.81 138	20.54 172	23.37 194	24.25 206	28.11 186	51.02 190	48.55 197	59.22 197	50.82 207
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 190	41.72 191	34.15 190	40.56 188	50.07 195	33.17 199	44.35 111	27.64 192	5.54 215	30.84 163	26.67 197	14.99 203	45.64 198	52.38 189	66.29 174	58.83 190

WR-MVS		37.61 191	42.15 190	32.31 199	43.64 180	51.85 189	29.39 208	43.35 126	27.65 191	4.40 217	29.90 171	29.80 187	10.46 210	46.73 197	51.98 191	62.60 191	57.16 194

TinyColmap		37.18 192	37.37 205	36.95 185	31.17 211	45.21 206	39.71 186	34.65 183	29.83 182	20.20 173	18.54 205	13.72 221	38.27 148	50.33 192	51.57 192	57.71 201	52.42 204

CP-MVSNet		37.09 193	40.62 193	32.99 192	37.56 194	48.25 200	32.75 200	43.05 131	27.88 189	5.93 211	31.27 161	25.82 202	15.09 201	43.37 205	48.82 195	63.54 186	58.90 188

DTE-MVSNet		36.91 194	40.44 194	32.79 195	40.74 186	47.55 202	30.71 206	44.39 108	27.03 194	4.32 218	30.88 162	25.99 200	12.73 208	45.58 199	50.80 193	63.86 183	55.23 200

PS-CasMVS		36.84 195	40.23 197	32.89 193	37.44 195	48.09 201	32.68 201	42.97 133	27.36 193	5.89 212	30.08 170	25.48 203	14.96 204	43.28 206	48.71 196	63.39 187	58.63 192

WR-MVS_H		36.29 196	40.35 196	31.55 201	37.80 193	49.94 197	30.57 207	41.11 144	26.90 195	4.14 219	30.72 165	28.85 191	10.45 211	42.47 207	47.99 201	65.24 177	55.54 198

SixPastTwentyTwo		36.11 197	37.80 201	34.13 191	37.13 198	46.72 204	34.58 194	34.96 181	21.20 208	11.66 195	29.15 177	19.88 214	29.77 182	44.93 200	48.34 198	56.67 204	54.41 202

test20.03		36.00 198	38.92 198	32.60 196	45.92 166	50.99 192	28.05 212	43.69 122	21.62 206	6.03 210	17.61 209	25.91 201	8.34 217	51.26 189	52.60 188	63.58 184	52.46 203

TDRefinement		35.76 199	38.23 199	32.88 194	19.09 221	46.04 205	43.29 177	29.49 204	33.49 164	19.04 183	22.29 198	17.82 216	29.69 184	48.60 194	47.24 204	56.65 205	52.12 205

LTVRE_ROB	32.83 17	35.10 200	37.46 202	32.35 198	43.12 182	49.99 196	28.52 210	33.23 197	12.73 220	8.18 206	27.71 182	21.34 211	32.64 172	46.92 196	48.11 199	48.41 211	55.45 199
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
PM-MVS		34.96 201	38.17 200	31.22 202	22.78 216	40.82 211	33.56 197	23.61 214	29.16 184	21.43 169	28.00 180	21.43 210	31.90 174	44.33 203	42.12 209	54.07 209	61.34 184

testgi		34.51 202	37.42 203	31.12 203	47.37 162	50.34 194	24.38 217	41.21 142	20.32 210	5.64 214	20.56 200	26.55 198	8.06 218	49.28 193	52.65 187	60.05 196	42.23 213

MDA-MVSNet-bldmvs		34.31 203	34.11 209	34.54 189	24.73 214	49.66 198	33.42 198	43.03 132	21.59 207	11.10 199	19.81 203	12.68 222	31.41 175	35.59 213	48.05 200	63.56 185	51.39 206

N_pmnet		34.09 204	35.74 207	32.17 200	37.25 197	43.17 209	32.26 204	35.57 178	26.22 198	10.60 201	20.44 202	19.38 215	20.20 197	44.59 202	47.00 205	57.13 203	49.35 210

RPSCF		33.61 205	40.43 195	25.65 207	16.00 223	32.41 217	31.73 205	13.33 221	50.13 83	23.12 159	31.56 157	40.09 138	32.73 171	41.14 211	37.05 212	36.99 217	50.63 208

EU-MVSNet		33.00 206	36.49 206	28.92 204	33.10 209	42.86 210	29.32 209	35.99 175	22.94 203	5.83 213	25.29 189	24.43 205	15.21 200	41.22 210	41.65 211	54.08 208	57.01 195

pmmvs3		31.22 207	33.62 210	28.43 205	22.82 215	40.26 214	26.40 213	22.05 216	16.89 216	10.99 200	14.72 214	16.26 217	29.70 183	44.82 201	47.39 203	58.61 199	54.98 201

FC-MVSNet-test		30.97 208	37.38 204	23.49 210	37.42 196	33.68 216	19.43 219	39.27 153	31.37 175	1.67 225	38.56 121	28.85 191	6.06 221	41.40 208	43.80 208	37.10 216	44.03 212

new-patchmatchnet		30.47 209	32.80 212	27.75 206	36.81 199	43.98 207	24.85 215	39.29 152	20.52 209	4.06 220	15.94 213	16.05 218	9.57 212	41.32 209	42.05 210	51.94 210	49.74 209

MIMVSNet1		29.60 210	33.37 211	25.20 209	19.52 219	43.94 208	26.29 214	37.92 166	19.95 213	3.79 221	12.64 218	21.99 209	7.70 219	43.83 204	46.32 207	55.97 206	44.92 211

FPMVS		26.87 211	28.19 213	25.32 208	27.09 213	29.49 219	32.28 203	17.79 218	28.09 188	11.33 196	19.38 204	14.69 219	20.88 196	35.11 214	32.82 215	42.56 213	37.75 214

WB-MVS		22.51 212	25.28 214	19.27 213	35.74 203	31.57 218	11.45 222	40.75 148	15.01 218	0.98 228	20.48 201	12.53 223	1.77 223	36.11 212	35.01 214	24.91 220	26.27 217

PMVS	18.18 18	21.95 213	22.85 215	20.90 212	21.92 217	14.78 221	19.95 218	17.31 219	15.69 217	11.32 197	13.70 215	13.91 220	15.02 202	34.92 215	31.72 216	39.85 215	35.20 215
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
new_pmnet		19.10 214	22.71 216	14.89 215	10.93 225	24.08 220	14.22 220	13.94 220	18.68 214	2.93 222	12.84 217	11.27 224	11.94 209	30.57 217	30.58 217	35.38 218	30.93 216

Gipuma		17.16 215	17.83 217	16.36 214	18.76 222	12.15 224	11.97 221	27.78 208	17.94 215	4.86 216	2.53 225	2.73 229	8.90 215	34.32 216	36.09 213	25.92 219	19.06 220
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_method		13.92 216	17.14 218	10.16 218	1.69 228	6.92 227	11.25 223	5.74 222	22.41 204	8.11 207	10.40 219	20.91 212	13.73 207	22.17 218	13.98 220	20.44 221	23.18 218

PMMVS2		12.25 217	14.17 219	10.00 219	11.39 224	14.35 222	8.21 224	19.29 217	9.31 221	0.19 229	7.38 221	6.19 227	1.10 224	19.26 219	21.13 219	19.85 222	21.56 219

E-PMN		10.66 218	8.30 221	13.42 216	19.91 218	7.87 225	4.30 227	29.47 205	8.37 224	1.70 224	3.67 222	1.29 232	9.12 214	8.98 223	13.59 221	16.03 223	14.30 223

EMVS		10.15 219	7.67 222	13.05 217	19.22 220	7.77 226	4.48 225	29.34 206	8.65 223	1.67 225	3.55 223	1.36 231	9.15 213	8.15 224	11.79 223	14.44 224	12.43 224

MVE	10.35 19	9.76 220	11.08 220	8.22 220	4.43 226	13.04 223	3.36 228	23.57 215	5.74 225	1.76 223	3.09 224	1.75 230	6.78 220	12.78 221	23.04 218	9.44 225	18.09 221
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 221	0.01 223	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.01 226	0.00 231	0.02 226	0.00 233	0.00 227	0.01 225	0.01 224	0.00 228	0.03 225

test123		0.01 221	0.01 223	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.01 226	0.00 231	0.02 226	0.00 233	0.01 225	0.00 226	0.01 224	0.00 228	0.03 225

uanet_test		0.00 223	0.00 225	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.00 228	0.00 231	0.00 228	0.00 233	0.00 227	0.00 226	0.00 226	0.00 228	0.00 227

sosnet-low-res		0.00 223	0.00 225	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.00 228	0.00 231	0.00 228	0.00 233	0.00 227	0.00 226	0.00 226	0.00 228	0.00 227

sosnet		0.00 223	0.00 225	0.00 222	0.00 230	0.00 230	0.00 231	0.00 226	0.00 228	0.00 231	0.00 228	0.00 233	0.00 227	0.00 226	0.00 226	0.00 228	0.00 227

TPM-MVS					78.45 5	83.50 6	78.26 3			58.88 7	72.62 17	77.54 9	69.42 4			80.40 7	85.71 44
Ray Leroy Khuboni and Hongjun Xu: Textureless Resilient Propagation Matching in Multiple View Stereosis (TPM-MVS). SATNAC 2025
RE-MVS-def										21.59 168

9.14												80.07 5

SR-MVS					63.74 45			48.51 60				73.80 20

Anonymous202405211			56.81 113		60.91 70	73.48 61	59.82 85	48.68 57	39.26 132		24.00 193	46.77 108	50.73 86	65.28 80	65.72 82	75.37 84	83.17 66

our_test_3					49.68 149	61.50 158	45.84 170

ambc			35.52 208		38.36 189	40.40 213	28.38 211		25.20 201	14.87 190	13.22 216	7.54 226	19.34 198	55.63 175	47.79 202	47.91 212	58.89 189

MTAPA										54.82 17		71.98 25

MTMP										50.64 31		68.31 30

Patchmatch-RL test							0.69 230

tmp_tt				4.41 221	2.56 227	1.81 229	2.61 229	0.27 224	20.12 211	9.81 203	17.69 208	9.04 225	1.96 222	12.88 220	12.11 222	9.23 226

XVS					62.70 54	73.06 64	61.80 71			42.02 74		63.42 43				74.68 99

X-MVStestdata					62.70 54	73.06 64	61.80 71			42.02 74		63.42 43				74.68 99

mPP-MVS					63.08 50							62.34 46

NP-MVS									72.62 28

Patchmtry						64.49 138	52.06 141	34.21 188		31.74 121

DeepMVS_CX						5.87 228	4.32 226	1.74 223	9.04 222	1.30 227	7.97 220	3.16 228	8.56 216	9.74 222		6.30 227	14.51 222

High-resolution multi-view benchmark